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Record W2326491169 · doi:10.5304/jafscd.2013.032.011

Increased Productivity, Role in Alleviating Food Insecurity Possible

2013· article· en· W2326491169 on OpenAlexaboutno aff
Kathryn Colasanti, Michael W. Hamm

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityAgricultural economicsSquare (algebra)Unit (ring theory)AgricultureFood insecurityGeographyFood securityEconomicsEconomic growthMathematicsArchaeology

Abstract

fetched live from OpenAlex

First paragraphs It's true that urban agriculture may provide a modest contribution to most cities' food supply. However, Hallsworth and Wong (2013) fail to recognize the range of cities across North America as well as the numerous opportunities to increase the productivity of urban agriculture and its potential role in alleviating food insecurity. They also under¬emphasize the value of urban agriculture beyond the quantity of food produced. There are many cities — Detroit, Cleveland, and Milwaukee come to mind — with large amounts of open space and notions of incorporating agriculture into the fabric of a 21st century green city. The authors fail to acknowledge the potential for expanded productivity per unit of land beyond what is currently observed, for example with the use of passive solar, season-extension methods. In Michigan, with average low temperatures below Vancouver's, unheated hoophouses allow for at least 30 crops to be grown, many year-round (Colasanti, Matts, Blackburn, Corrin, & Hausler, 2010). The authors dismiss what can be grown in a 4-square-meter (43-square-feet) garden as "suitable only for... personal enjoyment," but during the frost-free period an extra vegetable serving for a family of four per day is easily accomplished in this space....

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.191
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

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